{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/l-pagerank-for-semi-supervised-learning","title":"$L^γ$-PageRank for Semi-Supervised Learning","arxiv_id":"1903.06007","date":"2019-03-11","proceeding":null,"authors":["Esteban Bautista","Patrice Abry","Paulo Gonçalves"],"abstract":"PageRank for Semi-Supervised Learning has shown to leverage data structures\nand limited tagged examples to yield meaningful classification. Despite\nsuccesses, classification performance can still be improved, particularly in\ncases of fuzzy graphs or unbalanced labeled data. To address such limitations,\na novel approach based on powers of the Laplacian matrix $L^\\gamma$ ($\\gamma >\n0$), referred to as $L^\\gamma$-PageRank, is proposed. Its theoretical study\nshows that it operates on signed graphs, where nodes belonging to one same\nclass are more likely to share positive edges while nodes from different\nclasses are more likely to be connected with negative edges. It is shown that\nby selecting an optimal $\\gamma$, classification performance can be\nsignificantly enhanced. A procedure for the automated estimation of the optimal\n$\\gamma$, from a unique observation of data, is devised and assessed.\nExperiments on several datasets demonstrate the effectiveness of both\n$L^\\gamma$-PageRank classification and the optimal $\\gamma$ estimation.","url_abs":"http://arxiv.org/abs/1903.06007v1","url_pdf":"http://arxiv.org/pdf/1903.06007v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"l-pagerank-for-semi-supervised-learning","repo_url":"https://github.com/estbautista/Lgamma-PageRank_Paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}